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<h1 id="product">LingPipe</h1><h1 id="pagetitle">API Tutorials</h1>
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<h2>About the API Tutorials</h2>






<p>
The application program interface (API) turorials are intended to help
developers get started with the LingPipe API.   Each tutorial is designed
to stand alone.
</p>

<h3>Included Data and Precompiled Scripts</h3>
<p>
Most of the tutorials come with sample data, precompiled jars and an
example that works out of the box.  Tutorials which require third-party
data or software with restricted distribution (e.g.  MySQL) are noted
below.
</p>


<h2>The Tutorials</h2>

<p>
This section provides a list of available tutorials:
</p>

<div class="sidebar">
<h2>Code Style Warnings</h2>
<p>
The tutorial code is designed to be self-contained and clear rather
than robust.  In particular, it:
</p>
<ul>
<li>
prints exceptions and exits rather than
logging, recovering, and/or releasing resources,
</li>
<li>
uses static and/or hard-coded constructs that would
normally be configurable, 
</li>
<li>
greedily consumes memory rather than streaming, and
</li>
<li>
doesn't check input consistency or guarantee object
consistency.
</li>
</ul>
</div>



<h3><a href="classify/read-me.html">Topic Classification</a></h3>
<p>
Categorization of news articles by genre using character language models.
</p>

<h3><a href="ne/read-me.html">Named Entity Recognition</a></h3>
<p>
How to run named-entity recognizers in first-best, n-best and
per-entity confidence modes.  How to train and evaluate named-entity
recognizers.  Examples with newswire in Spanish and genomics in English.
</p>

<h3><a href="cluster/read-me.html">Clustering</a></h3>
<p>
An illustration of the single-link and complete-link hierarchical
clusterers, including a variety of cluster evaluation techniques.
There is an example of using clustering for cross-document coreference, with
an example application resolving different John Smiths in the news.
There is also an extensive tutorial on latent Dirichlet allocation (LDA).
</p>


<h3><a href="posTags/read-me.html">Part-of-Speech Tagging</a></h3>
<p>
 How to train part-of-speech (POS) taggers from corpora using tag
 parsers and handlers, how to compile models to disk and read them in,
 and how
to run and evaluate first-best, n-best and confidence-scored
 taggers.  Examples include the Brown, Genia, and GenTag
 part-of-speech corpora.
</p>

<h3><a href="sentences/read-me.html">Sentence Detection</a></h3>
<p>
How to run sentence detection using the chunking interface, how to
evaluate the performance of a sentence model against a corpu s using
sentence chunk parsers and handlers, and how to tune a model for a
particular corpus.  Examples from the Genia corpus.
</p>

<h3><a href="querySpellChecker/read-me.html">Spelling Correction</a></h3>
<p>
&quot;Did you mean&quot;-style search engine spell checking.  How to
train and tune a model.
</p>

<h3><a href="db/read-me.html">Database Text Mining</a></h3>
<p>
Part one populates a MySQL database with MEDLINE citations using
JDBC. Part two runs over a database of documents to create
tables of
sentences and entities.  Part three shows how to do text data mining
through database queries.
<br />
<span class="smallnote">[Requires GNU-licensed MySQL.]</span>
</p>

<h3><a href="stringCompare/read-me.html">String Comparison</a></h3>
<p>
How to use distance and proximity measures over strings, including
weighted edit disance, TF/IDF distance, Jaccard distance, Jaro-Winkler
distance, etc.
</p>

<h3><a href="interestingPhrases/read-me.html">Interesting Phrase Detection</a></h3>
<p>
Extraction of statistically significant multi-word phrases in one
corpus and of relatively significant (&quot;hot&quot;) terms in one
corpus relative to another.  
</p>

<h3><a href="lm/read-me.html">Character Language Modeling</a></h3>
<p>
Training and tuning character language models, extending
<code>com.aliasi.util.AbstractCommand</code> and using the
<code>com.aliasi.corpus.TextHandler</code> and
<code>com.aliasi.corpus.Parser</code> interfaces.
</p>

<h3><a href="chineseTokens/read-me.html">Chinese Word Segmentation</a></h3>
<p>
Shows how to segment a stream of Chinese characters into distinct
words.  The demo uses the standard LingPipe spelling corrector with an
edit distance tuned for word segmentation.  Shows how to train and
evaluate using publicly available training corpora from the First and
Second International Chinese Word Segmentation Bakeoffs.
<br />
<span class="smallnote">[Requires SigHan data download.]</span>
</p>

<h3><a href="hyphenation/read-me.html">Hyphenation and Syllabification</a></h3>
<p>
Shows how to train a hyphenator or syllabifier from dictionary
training data.  Examples in Dutch, English and German.
</p>


<h3><a href="sentiment/read-me.html">Sentiment Analysis</a></h3>
<p>
Uses language model classifiers to do sentiment analysis over movie
reviews.  Whole movie reviews are classified by polarity (thumbs up or
thumbs down), and single sentences are classified with respect to
subjectivity (subjective/opinion or objective/fact).  Walks through
compiling models and reading the extensive output produced by the
classifier evaluators.  Also explains hierarchical classification
which stacks the polarity classifier on top of the subjectivity
classifier for improved performance.  Discusses binomial confidence
intervals and the danger of <i>a posteriori</i> parameter setting.
<br />
<span class="smallnote">[Requires sentiment data download.]</span>
</p>


<h3><a href="langid/read-me.html">Language Identification</a></h3>
<p>
Language identification as a classification problem.  How to train
and evaluate language identifiers, with examples from the Leipzig
corpus of 15 languages.
</p>

<h3><a href="svd/read-me.html">Singular Value Decomposition</a></h3>
<p>
Use singular value decomposition to factor matrices.  Explains
how to deal with unknown value imputation, regularization and
setting tuning parameters.
</p>

<h3><a href="logistic-regression/read-me.html">Logistic Regression</a></h3>
<p>
How to estimate regularized multinomial logistic regression
models for discriminitive classification.
</p>

<h3><a href="em/read-me.html">Expectation Maximization</a></h3>
<p>
How to use expectation maximization for semi-supervised learning
for a variety of tasks.
</p>

<h3><a href="wordSense/read-me.html">Word Sense Disambiguation</a></h3>
<p>
Word sense disambiguation is the process of determing which
of a word's possible meanings is intended by a particular
instance of the word.  Word sense disambiguation has applications
for classification, search, clustering, etc.
</p>


<h3><a href="eclipse/read-me.html">Eclipse</a></h3>
<p>
Basic instructions on how to compile and test LingPipe using
the Eclipse integrated development environment (IDE).
</p>

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